Optimal Allocation of Human Resources Recommendation Based on Improved Particle Swarm Optimization Algorithm

Wei, Jintong

doi:10.1155/2022/2010685

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…For tasks of higher complexity, metaheuristic approaches such as the Non-Dominated Sorting genetic Algorithm II (NSGA II) and PSO have proven effective [35,39]. For instance, Feng et al implemented NSGA II to improve staffing in emergency departments [40], while Wei et al combined Ant Colony Optimization (ACO) with PSO to address human resource allocation challenges [41]. Additionally, well-established metaheuristic strategies like Tabu Search (TS) [42,43] and Simulated Annealing (SA) [44,45] have been effectively applied to the HRAP.…”

Section: Related Literaturementioning

confidence: 99%

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

Yang,

Teshome,

Yeh

et al. 2024

CMC

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In this study, we introduce a novel multi-objective optimization model tailored for modern manufacturing, aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance. Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel, addressing a crucial gap in the integration of maintenance personnel dispatching and station selection. Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness. The core of our methodology is the NSGA III+ Dispatch, an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), meticulously designed for the selection of maintenance stations and effective operator dispatching. This method integrates a comprehensive coding process, crossover operator, and mutation operator to efficiently manage multiple objectives. Rigorous empirical testing, including a detailed analysis from a Taiwanese electronic equipment manufacturer, validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations. The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23% in low-frequency and 28.23% in high-frequency machine failure scenarios, leading to notable improvements in efficiency and cost reduction. Additionally, it demonstrates significant improvements in operational efficiency, particularly in selective high-frequency failure contexts, while ensuring substantial manpower cost savings without compromising on operational effectiveness. This research significantly advances maintenance strategies in production environments, providing the manufacturing industry with practical, optimized solutions for diverse machine malfunction situations. Furthermore, the methodologies and principles developed in this study have potential applications in various other sectors, including healthcare, transportation, and energy, where maintenance efficiency and resource optimization are equally critical.

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Section: Related Literaturementioning

confidence: 99%

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

Yang,

Teshome,

Yeh

et al. 2024

CMC

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“…The particle swarm algorithm is a new optimization algorithm that integrates the particle swarm optimization algorithm and genetic algorithm [15], and its biggest feature is that it has fewer parameter settings and can run in two algorithms at the same time, which greatly simplifies the process of implementing the algorithm, and improves the operation efficiency. Evolutionary computation theory and swarm intelligence theory guided the development of a new type of intelligent optimization algorithm called particle swarm optimization algorithm.…”

Section: Weight Determination Model Based On Particle Swarm Algorithmmentioning

confidence: 99%

Based on the integration of multimedia e-commerce environment in the enterprise management innovation technology applications

2023

Applied Mathematics and Nonlinear Sciences

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This paper first applies Multi-Agents’ distributed data management model to establish an enterprise economic data mining platform, combined with a clustering algorithm to extract valuable economic data from a multi-source heterogeneous enterprise database. The weights of indicators are solved by particle cluster algorithm, and the linkage vector distance and set-pair analysis of linkage degree are introduced to construct the innovative technology of enterprise economics and management under the integrated multimedia e-commerce environment. The study shows that the enterprise macro optimal economic index weight of this paper’s method is 0.812, and the production qualification rate of the enterprise is as high as 98.8%, which indicates that this paper’s method can meet the innovation of the enterprise development, and realize the enterprise business management and development strategy is of great significance.

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“…The particle swarm optimization algorithm is widely used for clustering analysis of data in the research of Russian language talent training mode in the context of big data statistical analysis of the Hainan Free Trade Port [21].…”

Section: Particle Swarm Optimization Algorithm Solutionmentioning

confidence: 99%

Research on the cultivation mode of Russian language talents in the context of Hainan Free Trade Port based on big data statistical analysis

Huang

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, a talent training model based on big data analysis is designed for the background of the construction of the Hainan Free Trade Port. A learning behavior analysis method is designed using the K-Means clustering algorithm and particle swarm optimization algorithm, which can accurately mine valuable information from a large amount of user learning behavior data and provide a reference for the exploration of the Russian talent training model. The accuracy rate in the experimental validation can reach 91.99%, which is outstanding and can provide important support for establishing a systematic talent training model in the context of the Hainan Free Trade Port.

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Optimal Allocation of Human Resources Recommendation Based on Improved Particle Swarm Optimization Algorithm

Cited by 8 publications

References 27 publications

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

Based on the integration of multimedia e-commerce environment in the enterprise management innovation technology applications

Research on the cultivation mode of Russian language talents in the context of Hainan Free Trade Port based on big data statistical analysis

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